A Fast Approximation Scheme for Probabilistic Wavelet Synopses

Several studies have demonstrated the effectiveness of Haar wavelets in reducing large amounts of data down to compact wavelet synopses that can be used to obtain fast, accurate approximate query answers. While Haar wavelets were originally designed for minimizing the overall root-mean-squared (i.e.,  ¢¡ norm) error in the data approximation, the recently-proposed idea of probabilistic wavelet synopses also enables their use in minimizing other error metrics, such as the relative error in individual data-value reconstruction, which is arguably the most important for approximate query processing. Known construction algorithms for probabilistic wavelet synopses employ probabilistic schemes for coefficient thresholding that are based on optimal DynamicProgramming (DP) formulations over the error-tree structure for Haar coefficients. Unfortunately, these (exact) schemes can scale quite poorly for large data-domain and synopsis sizes. To address this shortcoming, in this paper, we int...

• Type: Conference
• Year: 2005
• Where: SSDBM
• Authors: Antonios Deligiannakis, Minos N. Garofalakis, Nick Roussopoulos
• Keywords: Database, Haar Wavelets, Probabilistic Wavelet Synopses, SSDBM 2005, Wavelet